Joint PhD Program in Machine Learning & Public Policy
With the critical importance of addressing global policy problems ranging from disease pandemics to crime and terrorism, and the continuously increasing size and complexity of policy data, the use of machine learning has become essential for data-driven policy analysis and for development of new, practical information technologies that can be directly applied for the public good. The numerous challenges facing our world will require broad, successful innovations at the intersection of machine learning and public policy, to develop novel methods which address critical policy challenges.
The Joint PhD Program in Machine Learning and Public Policy is a new program for students to gain the skills necessary to develop new state-of-the-art machine learning technologies and apply these successfully to real-world policy issues.
This PhD program differs from the ML PhD program in that it places significantly more emphasis on preparation in fields such as economics, organizational behavior, management science, operations research, and substantive policy domains such as health care and crime. Similarly, this program differs from the Public Policy PhD program in its emphasis on machine learning and computer science.
Students in this program will be involved in courses and research from both the Machine Learning Department and the Heinz College. Students are expected both to make fundamental contributions to the science of machine learning as well as addressing core problems in one or more policy domains.
A sample curriculum is as follows:
FALL - 1st Year
SPRING - 1st Year
|10-715 Adv. Machine Learning||15-750 Algorithms|
|10-705 Intermediate Statistics||10-702 Statistical Machine Learning|
|90-908 Microeconomics||Social Science Course
|90-901 Heinz PhD Seminar I||90-902 Heinz PhD Seminar II|
FALL - Year Two
SPRING - Year Two
|Heinz Advanced Elective
||15-826 Databases & Data Mining|
|ML/Stat Advanced Elective
||ML/PP Advanced Elective
|90-918 Heinz PhD Seminar III
Students must complete their first and second Heinz Research Papers by the end of year 2 and year 3 respectively.
Years 3 & 4
Thesis research co-supervised by a faculty in ML and a faculty in the Heinz College.
Applying to the Joint Program in Machine Learning & Public Policy
To apply to the joint program:
To be admitted to the Joint Program, the student must be accepted by both Admissions Committees from the Machine Learning Department and Heinz College.